#AWS

Amazon Web Services (AWS) is used alongside GCP in multi-cloud architectures. Articles cover banking-grade Terraform deployments on AWS, cost optimisation across cloud providers, and the infrastructure patterns specific to AWS financial services workloads.

17 posts tagged with aws. ← All posts

#A2A (14)#ADK (8)#AG-UI (6)#AI (9)#AI Agents (311)#AI Architecture (10)#AI Cost (10)#AI Cost Optimization (8)#AI Engineering (201)#AI Evaluation (8)#AI Governance (29)#AI Red Teaming (9)#AI Research (9)#AI Safety (8)#AI Security (21)#AI in Production (12)#AML (3)#API Design (10)#API Security (8)#APIs (55)#AWS (17)#Accounting (9)#Agent Skills (3)#Agentic AI (24)#Agentic Commerce (12)#Agentic RAG (8)#Agents (4)#Amazon Bedrock (8)#Analytics (3)#Architecture (36)#Audit (3)#Authentication (11)#Authorization (3)#Automation (6)#Azure (11)#Azure AI Foundry (9)#Backend Engineering (310)#Benchmarks (3)#Best Practices (3)#BigQuery (6)#Business Finance (8)#Business Strategy (55)#C (8)#CI/CD (8)#Caching (10)#Capital Markets (14)#Card Payments (12)#Cards (13)#Career (24)#Checkpointing (4)#Claude Code (8)#Cloud (5)#Cloud Architecture (3)#Cloud Native (10)#Code Review (8)#Collaboration (5)#Communication (9)#Compliance (52)#Computer Networking (9)#Computer Science (32)#Computer Vision (5)#Concurrency (37)#Consulting (3)#Containers (10)#Context Engineering (10)#Cost Optimisation (5)#Credit (14)#Credit Risk (14)#CrewAI (8)#Crypto (12)#Cryptocurrency (12)#Cryptography (8)#Custody (9)#DSPy (8)#Data (13)#Data Engineering (12)#Data Structures (9)#Databases (37)#Deployment (3)#Design Patterns (10)#DevOps (16)#DevSecOps (11)#Developer Experience (5)#Developer Tools (5)#Distributed Systems (94)#Documentation (3)#Edge AI (8)#Embeddings (15)#Emotional Intelligence (8)#Energy (8)#Engineering (11)#Engineering Culture (3)#Engineering Practices (16)#Error Handling (4)#Evaluation (53)#Event-Driven Architecture (8)#FREE-AI (8)#FX (5)#Feedback (4)#FinOps (23)#FinTech (6)#Financial AI (14)#Financial Systems (129)#Fine-Tuning (11)#Fintech (131)#Flutter (8)#Foreign Exchange (5)#Forward Deployed Engineer (8)#Forward Deployed Engineering (8)#Fraud (10)#Function Tools (5)#Functional Programming (3)#Fundraising (8)#GCP (5)#Gemma (4)#Generative AI (3)#Git (8)#Go (220)#Go-to-Market (8)#Google ADK (36)#Governance (56)#Granite (6)#GraphQL (3)#Growth (3)#Guardrails (25)#HIPAA (3)#HTTP (3)#Harness Engineering (8)#Hiring (8)#Hugging Face (8)#Human-in-the-Loop (8)#IBM watsonx (8)#Identity (11)#Integration (3)#Intellectual Property (8)#Interfaces (3)#JavaScript (8)#KYC (11)#KYC and AML (12)#Kafka (10)#Kubernetes (17)#LLM (5)#LLM Inference (8)#LLM-as-Judge (3)#LLMs (135)#LangChain (8)#LangGraph (11)#Leadership (26)#Ledger (12)#Legal (8)#Lending (14)#LlamaIndex (8)#MCP (22)#MLOps (31)#Machine Learning (32)#Marketing (16)#Markets (4)#Memory (15)#Memory Management (5)#Metrics (6)#Microservices (3)#Microsoft Agent Framework (150)#Middleware (6)#Migration (9)#Monitoring (3)#Multi-Agent (10)#Multi-Agent AI (14)#Multi-Agent Systems (73)#Multimodal (3)#Multimodal AI (8)#NIM (5)#NVIDIA (8)#OAuth (3)#OWASP (7)#Observability (44)#On-Device AI (8)#Open Source (7)#OpenTelemetry (5)#Operating Systems (8)#Operations (10)#Opinion (6)#Orchestration (10)#Organizational Design (8)#Payment Rails (16)#Payments (54)#People (8)#Performance (43)#Platform Engineering (9)#PreSales (8)#Privacy (5)#Privacy Engineering (3)#Process (4)#Product (27)#Product Management (8)#Production (8)#Programming (10)#Programming Languages (48)#Prompt Engineering (66)#Prompt Injection (6)#Protocol Buffers (3)#Protocols (9)#Providers (4)#Pydantic AI (8)#Python (142)#Quality (3)#RAG (58)#RBI (3)#REST (5)#Rails (16)#Reasoning Models (8)#Reconciliation (3)#RegTech (8)#Regulation (9)#Reliability (48)#Resilience (4)#Responsible AI (5)#Retrieval (3)#Risk (13)#Rust (32)#SRE (21)#Sales (9)#Scalability (3)#Security (81)#Security Engineering (8)#Self-Evolving Agents (16)#Sessions (3)#Settlement (9)#Soft Skills (8)#Software (3)#Software Architecture (36)#Software Engineering (144)#Spanner (4)#Startups (30)#Strands (8)#Streaming (28)#Structured Output (4)#Sustainability (8)#System Design (32)#Systems Programming (48)#Testing (49)#Tool Use (22)#Tooling (4)#Tools (3)#Trading (8)#Treasury (6)#Type System (3)#Type Systems (11)#TypeScript (8)#Vector Databases (21)#Vector Search (11)#Venture Capital (8)#Version Control (8)#Web Development (6)#Workflows (14)#gRPC (13)#smolagents (8)
Pratik Dhanave · ·5 min read

Strands in Practice

Strands is the right framework when you want to trust a capable model to drive and get out of its way — and the wrong one when you need to guarantee a process. This closing post gives the honest verdict on when to reach for Strands, how it compares to its peers, and how the model-driven approach fits the wider agent landscape.

Strands is the right framework when you want to trust a capable model to drive and get out of its way — and the wrong one when you need to guarantee a process. The honest verdict on when to reach for Strands and how it compares.

#Strands#AI Agents#AWS#AI Architecture
Pratik Dhanave · ·6 min read

Observability and Production

A model-driven agent decides its own path, which means you cannot know what it did without watching — so observability isn't a nice-to-have in Strands, it's a requirement. Built on OpenTelemetry and shaped by AWS's own production use, Strands treats seeing inside the agent as first-class, because a loop you can't see is a loop you can't trust.

A model-driven agent decides its own path, so you cannot know what it did without watching — observability isn't a nice-to-have in Strands, it's a requirement. Built on OpenTelemetry and shaped by AWS's own production use.

#Strands#AI Agents#Observability#AWS#Reliability
Pratik Dhanave · ·6 min read

Multi-Agent Systems

One model-driven agent handles a lot, but some problems want a team — a specialist per subtask, or a coordinator delegating to workers. Strands builds multi-agent systems from the same minimal parts, most elegantly by making an agent a tool another agent can call, so the model-driven approach scales up without new machinery.

One model-driven agent handles a lot, but some problems want a team. Strands builds multi-agent systems from the same minimal parts, most elegantly by making an agent a tool another agent can call — the model-driven approach scaling up.

#Strands#AI Agents#Multi-Agent Systems#AWS
Pratik Dhanave · ·5 min read

Model Providers

The model drives a Strands agent, so which model you use is the single biggest determinant of how well it works — and Strands keeps that a swappable choice across providers rather than locking you to one. Model-agnosticism isn't a convenience here; in a model-driven framework it's foundational.

The model drives a Strands agent, so which model you use is the biggest determinant of how well it works — and Strands keeps that a swappable choice across providers. Model-agnosticism isn't a convenience here; it's foundational.

#Strands#AI Agents#AWS#LLMs
Pratik Dhanave · ·6 min read

Tools

In a model-driven agent, tools are everything the agent can actually do — the model supplies the reasoning, the tools supply the capability. Strands makes defining them almost trivial (decorate a Python function) and plugs into MCP's large ecosystem, so equipping an agent well becomes the developer's main lever.

In a model-driven agent, tools are everything the agent can do — the model supplies the reasoning, the tools supply the capability. Strands makes defining them trivial and plugs into MCP, so equipping an agent well is the developer's main lever.

#Strands#AI Agents#Tool Use#AWS#MCP
Pratik Dhanave · ·6 min read

The Agent Loop

Strands's agent loop is deliberately small: a prompt goes in, the model decides, tools run if needed, results feed back, and it repeats until the model is done. What makes it distinctive isn't the loop's shape — every agent has one — but that Strands exposes it plainly and lets the model drive it, with only three ingredients you provide.

Strands's agent loop is deliberately small: a prompt goes in, the model decides, tools run if needed, results feed back, and it repeats until the model is done. Three ingredients you provide, and a loop the model drives.

#Strands#AI Agents#Python#AWS#Software Engineering#Prompt Engineering
Pratik Dhanave · ·6 min read

The Model-Driven Approach

The model-driven approach is not just how Strands works — it's a stance on where intelligence should live in an agent. Put it in the model's reasoning, not in developer-authored control flow. This post unpacks why that stance is increasingly the right one, and where it isn't.

The model-driven approach is a stance on where intelligence should live in an agent: in the model's reasoning, not in developer-authored control flow. This post unpacks why that stance is increasingly right, and where it isn't.

#Strands#AI Agents#Python#AWS#Software Engineering
Pratik Dhanave · ·6 min read

What Is Strands Agents?

Most agent frameworks ask you to design the workflow — the steps, the branches, the orchestration. Strands Agents, AWS's open-source SDK, makes the opposite bet: give the model a goal and tools, and let it drive. That model-driven philosophy is the whole point, and understanding it is understanding why Strands feels different from everything else.

Most agent frameworks ask you to design the workflow. Strands Agents, AWS's open-source SDK, makes the opposite bet: give the model a goal and tools, and let it drive. That model-driven philosophy is the whole point.

#Strands#AI Agents#Python#AWS#Software Engineering
Pratik Dhanave · ·13 min read

Bedrock in Production: IAM, Cost, and Observability

Taking an Amazon Bedrock Go service from a working prototype to something you can run on-call — least-privilege IAM, credentials without static keys, tuning the SDK's built-in retryer, tracking token cost, and wiring up logging and metrics with aws-sdk-go-v2.

Taking a Bedrock Go service to production: least-privilege IAM and role-based credentials, tuning the SDK's built-in retryer for throttling, token-based cost tracking, and observability via model-invocation logging, structured metrics, and request IDs.

#Amazon Bedrock#AWS#Production#Go#AI Engineering#Backend Engineering#Observability
Pratik Dhanave · ·12 min read

Guardrails and Safety

How to put Amazon Bedrock Guardrails in front of a model from Go — attaching one to a Converse call, screening raw text with ApplyGuardrail, and reading whether the guardrail actually intervened.

Guardrails for Amazon Bedrock in Go: content filters, denied topics, PII/sensitive-information filters, and contextual grounding — attaching a guardrail to a Converse call and screening arbitrary text with ApplyGuardrail, checking for intervention.

#Amazon Bedrock#AWS#AI Safety#Go#AI Engineering#Backend Engineering#Guardrails
Pratik Dhanave · ·13 min read

Bedrock Agents

How to invoke a managed Agent for Amazon Bedrock from Go — where the server owns the reason-act loop, and your job is to call InvokeAgent, range the event stream, accumulate the answer chunks, and read the trace for observability.

Agents for Amazon Bedrock from Go: the managed reason-act loop that runs server-side (vs the DIY Converse loop), invoking an agent alias with InvokeAgent, streaming the response and trace events, and keeping multi-turn state with a SessionId.

#Amazon Bedrock#AWS#AI Agents#Go#AI Engineering#Backend Engineering#Streaming
Pratik Dhanave · ·10 min read

Retrieval-Augmented Generation with Knowledge Bases

How to query a Knowledge Base for Amazon Bedrock from Go — the managed retrieve-then-read layer — using both the low-level Retrieve call and the one-shot RetrieveAndGenerate, with citations wired through.

RAG on Bedrock in Go with Knowledge Bases: the retrieve-then-read pattern via Retrieve, the one-shot managed path via RetrieveAndGenerate with citations, and when to reach for each — plus reading grounding so you keep RAG's trust benefit.

#Amazon Bedrock#AWS#RAG#Go#AI Engineering#Backend Engineering
Pratik Dhanave · ·12 min read

Tool Use with the Converse API

How to give an Amazon Bedrock model real Go functions — declaring tools, catching the tool-use stop reason, executing your code, and returning results — using the full round-trip loop in aws-sdk-go-v2.

Giving a Bedrock model tools in Go via the Converse API: declaring a ToolConfiguration, the ToolUse round-trip loop, echoing ToolUseId, returning tool results as a user message, and handling parallel tool calls.

#Amazon Bedrock#AWS#Tool Use#Go#AI Engineering#Backend Engineering#APIs
Pratik Dhanave · ·9 min read

Streaming and Token Usage

How to stream Amazon Bedrock responses token-by-token with the aws-sdk-go-v2 Converse API, decode the event stream with a double type-switch, and account for tokens and cost from the metadata event — accurately, in Go.

Streaming responses and accounting for tokens and cost on Bedrock in Go: ranging the ConverseStream event stream, the nested delta unions, checking stream.Err(), and computing cost from the metadata usage event with a formula you fill in.

#Amazon Bedrock#AWS#LLMs#Go#AI Engineering#Backend Engineering#Streaming
Pratik Dhanave · ·11 min read

Calling a Model with the Converse API

Your first real inference call in Go against Amazon Bedrock — using the unified, model-agnostic Converse API and the AWS SDK for Go v2, from client construction to reading tokens back off the response.

Your first real inference call on Bedrock in Go via the unified Converse API: building the client, the ConverseInput message/content-block union, extracting the assistant text, and reading stop reason and token usage — with the content-block union explained.

#Amazon Bedrock#AWS#LLMs#Go#AI Engineering#Backend Engineering#APIs
Pratik Dhanave · ·9 min read

What Amazon Bedrock Is

The opener for a Go series on building LLM and agent applications with Amazon Bedrock — what the service actually is, why it sits between your Go code and a dozen foundation models, and which aws-sdk-go-v2 packages you will lean on for the rest of the way.

The opener to a series on building LLM and agent applications on Amazon Bedrock in Go: what Bedrock actually is, what it adds over calling a provider API directly (one API across models, IAM auth, data residency), and the aws-sdk-go-v2 packages you'll use.

#Amazon Bedrock#AWS#LLMs#Go#AI Engineering#Backend Engineering#APIs

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.